{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Y6Covr-GEHAR",
    "outputId": "3c223aae-fea0-4395-9e9e-a9a91b3f68a0"
   },
   "outputs": [],
   "source": [
    "!pip install -U accelerate bitsandbytes datasets transformers peft trl sentencepiece wandb langchain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "T_TZjUpIVxp0",
    "outputId": "aabdcb2d-c72d-4e7a-8171-3f32ac1aa901"
   },
   "outputs": [],
   "source": [
    "!pip show accelerate bitsandbytes datasets transformers peft trl sentencepiece wandb langchain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145,
     "referenced_widgets": [
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      "ffc00b5c3eb74da49e2f6830d838c57f",
      "2e3bef3a5da44904943ce1a6ca7b09b9",
      "f173d56d0f9c459abda33003ef360162"
     ]
    },
    "id": "f1k_uTqTF1jy",
    "outputId": "a50abbf9-e624-411b-fed8-463064ddb410"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e34a2a16c544c51ac5c0853d629728b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monitoring with Weights and Wiases (W&B)\n",
    "\n",
    "Weights and Biases (W&B) is an MLOps platform that can help developers monitor and document ML training workflows from end to end. As mentioned earlier, we will use W&B to get an idea of how well the training is working and if the model is improving over time. For W&B, we need to name the project; alternatively, we can use wandb's init() method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "kWgT2HHGZPfA"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"WANDB_PROJECT\"] = \"finetuning\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 124
    },
    "id": "mhPVjAl2EijY",
    "outputId": "42d265bf-378d-4b20-d957-3c77ae24354d"
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
       "            function loadScript(url) {\n",
       "            return new Promise(function(resolve, reject) {\n",
       "                let newScript = document.createElement(\"script\");\n",
       "                newScript.onerror = reject;\n",
       "                newScript.onload = resolve;\n",
       "                document.body.appendChild(newScript);\n",
       "                newScript.src = url;\n",
       "            });\n",
       "            }\n",
       "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
       "            const iframe = document.createElement('iframe')\n",
       "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
       "            document.body.appendChild(iframe)\n",
       "            const handshake = new Postmate({\n",
       "                container: iframe,\n",
       "                url: 'https://wandb.ai/authorize'\n",
       "            });\n",
       "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
       "            handshake.then(function(child) {\n",
       "                child.on('authorize', data => {\n",
       "                    clearTimeout(timeout)\n",
       "                    resolve(data)\n",
       "                });\n",
       "            });\n",
       "            })\n",
       "        });\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
      "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ··········\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import wandb\n",
    "wandb.login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "HxC2eHlLT_KA"
   },
   "outputs": [],
   "source": [
    "if wandb.run is not None:\n",
    "  wandb.finish()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hugging Face dataset\n",
    "\n",
    "Next, we’ll need to choose a dataset for our work. We can use lots of different datasets here that are appropriate for coding, storytelling, tool use, SQL generation, grade-school math questions (GSM8k), or many other tasks. Hugging Face provides a wealth of datasets, which can be viewed at this URL: https://huggingface.co/datasets. These cover a lot of different and even the most niche tasks.\n",
    "\n",
    "In this recipe, we are fine-tuning for question-answering performance with the `Squad V2` dataset. You can see a detailed [dataset description on Hugging Face](https://huggingface.co/spaces/evaluate-metric/squad_v2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 305,
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      "d0824d7aeda94cc6bd5f0c9a5739869a"
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    },
    "id": "MU9_bGIFQUeT",
    "outputId": "0ffc4929-91b4-44b1-e7f7-7d6a56c94d39"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed89e0816ca4462197ed55e275488e8b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/5.28k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2a4fd0e63d2477cae43a049b04f80b4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading metadata:   0%|          | 0.00/2.40k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c01e2f7a902c40b388f9fd49ec95e9b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/8.02k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7c21ee148ef410a8cf68649fcad8fb1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "135b5385c5b14d229637280b0bbfaf67",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d0a5cd1380714c76bd1af4386ab237ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e6c8097ff4d34d5099bb11739640d909",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee825de7dac64c26bfdd35f28b8cfb45",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/130319 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "95ffd24e4ce5441aa6c3f9ff2f2cf178",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating validation split:   0%|          | 0/11873 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset_name = \"squad_v2\"\n",
    "dataset = load_dataset(dataset_name, split=\"train\")\n",
    "eval_dataset = load_dataset(dataset_name, split=\"validation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AD1oFjKTSuSM",
    "outputId": "be29adf0-031e-4910-806a-15f03f6a874b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
       "        num_rows: 130319\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
       "        num_rows: 11873\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_dataset(dataset_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ezx6XVbCQcp_",
    "outputId": "3a6137f8-fbe1-4aa9-868f-2afa4ae4fe06"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': Value(dtype='string', id=None),\n",
       " 'title': Value(dtype='string', id=None),\n",
       " 'context': Value(dtype='string', id=None),\n",
       " 'question': Value(dtype='string', id=None),\n",
       " 'answers': Sequence(feature={'text': Value(dtype='string', id=None), 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None)}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hugging Face open-source models\n",
    "\n",
    "We want a small model that we can run locally at a decent token rate. `LLaMa-2` models require signing a license agreement with your email address and getting confirmed (which, to be fair, can be very fast), as it comes with restrictions for commercial use. LLaMa derivatives such as OpenLLaMa have performed quite well, as can be evidenced on the [HF leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)\n",
    "`OpenLLaMa` version 1 cannot be used for coding tasks, because of the tokenizer. Therefore, let’s use v2! We’ll use a 3B parameter model, which we’ll be able to use even on older hardware.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "vz5D8ndPOha9"
   },
   "outputs": [],
   "source": [
    "model_id = \"openlm-research/open_llama_3b_v2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 399
    },
    "id": "3uSvTxP0QhAO",
    "outputId": "79e74908-e049-4cd9-ef41-d984a49ffeb8"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from transformers import AutoModelForCausalLM, BitsAndBytesConfig\n",
    "\n",
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=torch.float16,\n",
    ")\n",
    "device_map=\"auto\"\n",
    "\n",
    "base_model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_id,\n",
    "    quantization_config=bnb_config,\n",
    "    device_map=\"auto\",\n",
    "    trust_remote_code=True,\n",
    ")\n",
    "base_model.config.use_cache = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "t8qxSsZcTz_H",
    "outputId": "1265d4ac-6dd7-4132-d4c1-c8ef2fe93ec7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LoRA fine-tuning\n",
    "\n",
    "Now, we’ll define our training configuration. We’ll set up LORA and other training arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 253,
     "referenced_widgets": [
      "11775873a0734a0591a84aa238e34c40",
      "eeb0bd7ad3664560beb9d88ac7ad806b",
      "af38836193eb47b98582bd294112175b",
      "6a6662babc38480eaa160649ed65891b",
      "59eb17756fbb4a2fa6c5a0bef81def63",
      "13666b2fc2934041a643b2fe89d1fb60",
      "c9e6d78db56b4f04a4225d337b1a5b1e",
      "02485fedd8334b4184dd23d7a25d102d",
      "44ca70e905eb49a7ae86df2f4216b742",
      "36f8d4ae4b284137aa17f2255090447c",
      "763e3accf2844c0db240aea37eb476e2"
     ]
    },
    "id": "WWIvXYm2QuMw",
    "outputId": "64e679b0-5dc0-402e-e870-1b290e4623c3"
   },
   "outputs": [
    {
     "data": {
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       "model_id": "11775873a0734a0591a84aa238e34c40",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/11873 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.15.8"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>/content/wandb/run-20230825_123411-1aev1nma</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/auffarth/finetuning/runs/1aev1nma' target=\"_blank\">lilac-gorge-4</a></strong> to <a href='https://wandb.ai/auffarth/finetuning' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/auffarth/finetuning' target=\"_blank\">https://wandb.ai/auffarth/finetuning</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/auffarth/finetuning/runs/1aev1nma' target=\"_blank\">https://wandb.ai/auffarth/finetuning/runs/1aev1nma</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='6' max='2000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [   6/2000 00:17 < 2:21:50, 0.23 it/s, Epoch 0.00/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>\n",
       "    <div>\n",
       "      \n",
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       "      [ 297/1485 03:10 < 12:45, 1.55 it/s]\n",
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       "    "
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       "<IPython.core.display.HTML object>"
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   ],
   "source": [
    "from transformers import AutoTokenizer, TrainingArguments, EarlyStoppingCallback\n",
    "from peft import LoraConfig\n",
    "from trl import SFTTrainer\n",
    "\n",
    "# More info: https://github.com/huggingface/transformers/pull/24906\n",
    "base_model.config.pretraining_tp = 1\n",
    "\n",
    "peft_config = LoraConfig(\n",
    "    lora_alpha=16,\n",
    "    lora_dropout=0.1,\n",
    "    r=64,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\",\n",
    ")\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.padding_side = \"right\"\n",
    "\n",
    "# if you don't want to use google drive, just set this to a directory on your computer:\n",
    "output_dir = \"/content/gdrive/My Drive/results\"\n",
    "new_model_name = f\"openllama-3b-peft-{dataset_name}\"\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "    output_dir=output_dir,  # use new_model_name if saving to hf\n",
    "    per_device_train_batch_size=4,\n",
    "    gradient_accumulation_steps=4,\n",
    "    learning_rate=2e-4,\n",
    "    logging_steps=10,\n",
    "    max_steps=2000,  # training can still improve after many steps!\n",
    "    num_train_epochs=100,\n",
    "    evaluation_strategy=\"steps\",\n",
    "    eval_steps=5,  # update steps between two evaluations\n",
    "    save_total_limit=5,  # only last 5 models are saved\n",
    "    push_to_hub=False,  # you can set this to true if you want to upload your model to huggingspace\n",
    "    load_best_model_at_end=True,  # to use in combination with early stopping\n",
    "    report_to=\"wandb\"\n",
    ")\n",
    "\n",
    "max_seq_length = 512\n",
    "\n",
    "trainer = SFTTrainer(\n",
    "    model=base_model,\n",
    "    train_dataset=dataset,\n",
    "    eval_dataset=eval_dataset,\n",
    "    peft_config=peft_config,\n",
    "    dataset_text_field=\"question\",  # this depends on the dataset!\n",
    "    max_seq_length=max_seq_length,\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_args,\n",
    "    callbacks=[EarlyStoppingCallback(early_stopping_patience=200)]\n",
    ")\n",
    "\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "e5gJjiIrm97E"
   },
   "outputs": [],
   "source": [
    "trainer.model.save_pretrained(\n",
    "    os.path.join(output_dir, \"final_checkpoint\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hhb8AOiQeSqU"
   },
   "outputs": [],
   "source": [
    "trainer.model.push_to_hub(\n",
    "    repo_id=new_model_name\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "imvnXAlxbGj8"
   },
   "outputs": [],
   "source": [
    "# usually, the peft model is stored as an adapter, not as a full model, therefore the loading is a bit different:\n",
    "from peft import PeftModel, PeftConfig\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
    "from langchain_community.llms import HuggingFacePipeline\n",
    "\n",
    "model_id = 'openlm-research/open_llama_3b_v2'\n",
    "config = PeftConfig.from_pretrained(\"benji1a/openllama-3b-peft-squad_v2\")\n",
    "model = AutoModelForCausalLM.from_pretrained(model_id)\n",
    "model = PeftModel.from_pretrained(model, \"benji1a/openllama-3b-peft-squad_v2\")\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "pipe = pipeline(\n",
    "    \"text-generation\",\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    "    max_length=256\n",
    ")\n",
    "llm = HuggingFacePipeline(pipeline=pipe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8PegBOhjprv0"
   },
   "source": [
    "# Commercial models\n",
    "\n",
    "So far, we’ve shown how to fine-tune and deploy an open-source LLM. Some commercial models can be fine-tuned on custom data as well.\n",
    "\n",
    "\n",
    "Fine-tuning a PaLM model for text classification can be done like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skllm.models.palm import PaLMClassifier\n",
    "clf = PaLMClassifier(n_update_steps=100)\n",
    "clf.fit(X_train, y_train) # y_train is a list of labels\n",
    "labels = clf.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, you can fine-tune the GPT-3.5 model for text classification like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skllm.models.gpt import GPTClassifier\n",
    "clf = GPTClassifier(\n",
    "        base_model = \"gpt-3.5-turbo-0613\",\n",
    "        n_epochs = None, # int or None. When None, will be determined automatically by OpenAI\n",
    "        default_label = \"Random\", # optional\n",
    ")\n",
    "clf.fit(X_train, y_train) # y_train is a list of labels\n",
    "labels = clf.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prompt engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zero-shot prompting\n",
    "\n",
    "Zero-shot prompting, as opposed to few-shot prompting (discussed next), involves feeding task instructions directly to an LLM without providing any demonstrations or examples. This prompt tests the capabilities of the pre-trained model to understand and follow the instructions:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_core.prompts import PromptTemplate\n",
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "\n",
    "model = ChatOpenAI()\n",
    "prompt = PromptTemplate(input_variables=[\"text\"], template=\"Classify the sentiment of this text: {text}\")\n",
    "chain = prompt | model\n",
    "print(chain.invoke({\"text\": \"I hated that movie, it was terrible!\"}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Few-shot learning\n",
    "\n",
    "Few-shot learning presents the LLM with just a few input-output examples relevant to the task, without explicit instructions.\n",
    "\n",
    "We provide it with a few examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "examples = [{\n",
    "    \"input\": \"I absolutely love the new update! Everything works seamlessly.\",\n",
    "    \"output\": \"Positive\",\n",
    "    },{\n",
    "    \"input\": \"It's okay, but I think it could use more features.\",\n",
    "    \"output\": \"Neutral\",\n",
    "    }, {\n",
    "    \"input\": \"I'm disappointed with the service, I expected much better performance.\",\n",
    "    \"output\": \"Negative\"\n",
    "}]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_core.prompts import FewShotPromptTemplate, PromptTemplate\n",
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "\n",
    "example_prompt = PromptTemplate(\n",
    "    template=\"{input} -> {output}\",\n",
    "    input_variables=[\"input\", \"output\"],\n",
    ")\n",
    "prompt = FewShotPromptTemplate(\n",
    "    examples=examples,\n",
    "    example_prompt=example_prompt,\n",
    "    suffix=\"Question: {input}\",\n",
    "    input_variables=[\"input\"]\n",
    ")\n",
    "print((prompt | ChatOpenAI()).invoke({\"input\": \" This is an excellent book with high quality explanations.\"}))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chain-of-thought prompting\n",
    "\n",
    "`CoT` prompting aims to encourage reasoning by getting the model to provide intermediate steps, leading to the definitive answer. This is done by prefixing the prompt with instructions to show its thinking. There are two variants of CoT, `zero-shot` and `few-shot`. \n",
    "\n",
    "In zero-shot CoT prompting, we just add the instruction “Let’s think step by step!” to the prompt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_core.prompts import PromptTemplate\n",
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "\n",
    "reasoning_prompt = \"{question}\\nLet's think step by step!\"\n",
    "prompt = PromptTemplate(\n",
    "  template=reasoning_prompt,\n",
    "  input_variables=[\"question\"]\n",
    ")\n",
    "model = ChatOpenAI()\n",
    "chain = prompt | model\n",
    "print(chain.invoke({\n",
    "   \"question\": \"There were 5 apples originally. I ate 2 apples. My friend gave me 3 apples. How many apples do I have now?\",\n",
    "}))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Few-shot CoT` prompting is a few-shot prompt, where the reasoning is explained as part of the example solutions, with the idea to encourage an LLM to explain its reasoning before deciding. If we go back to the few-shot examples from earlier, we can extend them as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "examples = [{\n",
    "    \"input\": \"I absolutely love the new update! Everything works seamlessly.\",\n",
    "    \"output\": \"Love and absolute works seamlessly are examples of positive sentiment. Therefore, the sentiment is positive\",\n",
    "    },{\n",
    "    \"input\": \"It's okay, but I think it could use more features.\",\n",
    "    \"output\": \"It's okay is not an endorsement. The customer further thinks it should be extended. Therefore, the sentiment is neutral\",\n",
    "    }, {\n",
    "    \"input\": \"I'm disappointed with the service, I expected much better performance.\",\n",
    "    \"output\": \"The customer is disappointed and expected more. This is negative\"\n",
    "}]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Self-consistency\n",
    "\n",
    "With self-consistency prompting, the model generates multiple candidate answers to a question. These are then compared against each other, and the most consistent or frequent answer is selected as the final output.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In the first step, we’ll create multiple solutions to a question or a problem:\n",
    "from langchain.chains import LLMChain\n",
    "from langchain_core.prompts import PromptTemplate\n",
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "\n",
    "solutions_template = \"\"\"\n",
    "Generate {num_solutions} distinct answers to this question:\n",
    "{question}\n",
    "\n",
    "\n",
    "Solutions:\n",
    "\"\"\"\n",
    "solutions_prompt = PromptTemplate(\n",
    "   template=solutions_template,\n",
    "   input_variables=[\"question\", \"num_solutions\"]\n",
    ")\n",
    "solutions_chain = LLMChain(\n",
    "   llm=ChatOpenAI(),\n",
    "   prompt=solutions_prompt,\n",
    "   output_key=\"solutions\"\n",
    ")\n",
    "\n",
    "# Then, we want to count the different answers. We can use an LLM again:\n",
    "consistency_template = \"\"\"\n",
    "For each answer in {solutions}, count the number of times it occurs. Finally, choose the answer that occurs most.\n",
    "\n",
    "\n",
    "Most frequent solution: \n",
    "\"\"\"\n",
    "consistency_prompt = PromptTemplate(\n",
    "   template=consistency_template,\n",
    "   input_variables=[\"solutions\"]\n",
    ")\n",
    "consistency_chain = LLMChain(\n",
    "   llm=ChatOpenAI(),\n",
    "   prompt=consistency_prompt,\n",
    "   output_key=\"best_solution\"\n",
    ")\n",
    "\n",
    "#Now, let’s put these two chains together with a SequentialChain. This runs two chains one by one in sequence. The first chain asks a question several times, and the second chain takes the answers of the first chain and outputs the final answer by choosing the answer that occurs most.\n",
    "from langchain.chains import SequentialChain\n",
    "\n",
    "\n",
    "answer_chain = SequentialChain(\n",
    "   chains=[solutions_chain, consistency_chain],\n",
    "   input_variables=[\"question\", \"num_solutions\"],\n",
    "   output_variables=[\"best_solution\"]\n",
    ")\n",
    "\n",
    "#As the last step, let’s ask a simple question and check the answer:\n",
    "print(answer_chain.invoke(\n",
    "   question=\"Which year was the Declaration of Independence of the United States signed?\",\n",
    "   num_solutions=\"5\"\n",
    "))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tree-of-thought\n",
    "\n",
    "In`Tree-of-Thought (ToT)` prompting, we generate multiple problem-solving steps or approaches for a given prompt and then use the AI model to critique them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#First, we’ll define our four chain components with PromptTemplate. We need a solution template, an evaluation template, a reasoning template, and a ranking template. Let’s first generate solutions:\n",
    "solutions_template = \"\"\"\n",
    "Generate {num_solutions} distinct solutions for {problem}. Consider factors like {factors}.\n",
    "\n",
    "\n",
    "Solutions:\n",
    "\"\"\"\n",
    "solutions_prompt = PromptTemplate(\n",
    "   template=solutions_template,\n",
    "   input_variables=[\"problem\", \"factors\", \"num_solutions\"]\n",
    ")\n",
    "Let’s ask the LLM to evaluate these solutions:\n",
    "evaluation_template = \"\"\"\n",
    "Evaluate each solution in {solutions} by analyzing pros, cons, feasibility, and probability of success.\n",
    "\n",
    "\n",
    "Evaluations:\n",
    "\"\"\"\n",
    "evaluation_prompt = PromptTemplate(\n",
    "  template=evaluation_template,\n",
    "  input_variables=[\"solutions\"] \n",
    ")\n",
    "\n",
    "# After this, we want to reason a bit more about them:\n",
    "reasoning_template = \"\"\"\n",
    "For the most promising solutions in {evaluations}, explain scenarios, implementation strategies, partnerships needed, and handling potential obstacles.\n",
    "\n",
    "\n",
    "Enhanced Reasoning:\n",
    "\"\"\"\n",
    "reasoning_prompt = PromptTemplate(\n",
    "  template=reasoning_template,\n",
    "  input_variables=[\"evaluations\"]\n",
    ")\n",
    "\n",
    "#Finally, we can rank these solutions given our reasoning so far:\n",
    "ranking_template = \"\"\"\n",
    "Based on the evaluations and reasoning, rank the solutions in {enhanced_reasoning} from most to least promising.\n",
    "\n",
    "\n",
    "Ranked Solutions:\n",
    "\"\"\"\n",
    "ranking_prompt = PromptTemplate(\n",
    "  template=ranking_template,\n",
    "  input_variables=[\"enhanced_reasoning\"]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create chains from these templates before we put the chains all together:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains import LLMChain\n",
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "\n",
    "solutions_chain = LLMChain(\n",
    "   llm=ChatOpenAI(),\n",
    "   prompt=solutions_prompt,\n",
    "   output_key=\"solutions\"\n",
    ")\n",
    "evalutation_chain = LLMChain(\n",
    "   llm=ChatOpenAI(),\n",
    "   prompt=evaluation_prompt,\n",
    "   output_key=\"evaluations\"\n",
    ")\n",
    "reasoning_chain = LLMChain(\n",
    "   llm=ChatOpenAI(),\n",
    "   prompt=reasoning_prompt,\n",
    "   output_key=\"enhanced_reasoning\"\n",
    ")\n",
    "ranking_chain = LLMChain(\n",
    "   llm=ChatOpenAI(),\n",
    "   prompt=ranking_prompt,\n",
    "   output_key=\"ranked_solutions\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please note how each output_key corresponds to an input_key in the prompt of the following chain. Finally, we connect these chains into a `SequentialChain`:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains import SequentialChain\n",
    "\n",
    "tot_chain = SequentialChain(\n",
    "   chains=[solutions_chain, evalutation_chain, reasoning_chain, ranking_chain],\n",
    "   input_variables=[\"problem\", \"factors\", \"num_solutions\"],\n",
    "   output_variables=[\"ranked_solutions\"]\n",
    ")\n",
    "print(tot_chain.invoke(\n",
    "   problem=\"Prompt engineering\",\n",
    "   factors=\"Requirements for high task performance, low token use, and few calls to the LLM\",\n",
    "   num_solutions=3\n",
    "))\n"
   ]
  }
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